On the predictive potential of kernel principal components

DOI10.1214/19-EJS1655zbMath1437.62145OpenAlexW2996823939MaRDI QIDQ2283584

Bing Li, Andreas Artemiou, Ben Jones

Publication date: 3 January 2020

Published in: Electronic Journal of Statistics (Search for Journal in Brave)

Full work available at URL: https://projecteuclid.org/euclid.ejs/1578020612

zbMATH Keywords

dimension reductionnonparametric regressionCauchy distributionunitary invariancekernel principal components

Mathematics Subject Classification ID

Nonparametric regression and quantile regression (62G08) Factor analysis and principal components; correspondence analysis (62H25) Density estimation (62G07) Interacting random processes; statistical mechanics type models; percolation theory (60K35)

Related Items

Revisiting the predictive power of kernel principal components

Uses Software

• Kernlab

Cites Work

• Unnamed Item
• Unnamed Item
• Unnamed Item
• Unnamed Item
• Unnamed Item
• Unnamed Item
• Unnamed Item
• Unnamed Item
• Unnamed Item
• Unnamed Item
• Sufficient dimension reduction via principal L\(q\) support vector machine
• Predictive power of principal components for single-index model and sufficient dimension reduction
• Rejoinder: Fisher lecture: Dimension reduction in regression
• Principal support vector machines for linear and nonlinear sufficient dimension reduction
• Kernel ridge vs. principal component regression: minimax bounds and the qualification of regularization operators
• An RKHS formulation of the inverse regression dimension-reduction problem
• Data spectroscopy: eigenspaces of convolution operators and clustering
• Dimension reduction strategies for analyzing global gene expression data with a response
• Principal component analysis.
• Smoothed functional principal components analysis by choice of norm
• A general theory for nonlinear sufficient dimension reduction: formulation and estimation
• Nonlinear sufficient dimension reduction for functional data
• On principal components regression with Hilbertian predictors
• Kernel dimension reduction in regression
• Completely random measures
• Principal component regression revisited
• A Biometrics Invited Paper. The Analysis and Selection of Variables in Linear Regression
• Ordering and Selecting Components in Multivariate or Functional Data Linear Prediction
• Principal Curves
• Theoretical Foundations of Functional Data Analysis, with an Introduction to Linear Operators
• Theory of Reproducing Kernels
• Comment: Fisher lecture: Dimension reduction in regression